// Small bench routine for Eigen available in Eigen
// (C) Desire NUENTSA WAKAM, INRIA

#include <Eigen/SparseLU>
#include <bench/BenchTimer.h>
#include <fstream>
#include <iomanip>
#include <iostream>
#include <unsupported/Eigen/SparseExtra>
#ifdef EIGEN_METIS_SUPPORT
#include <Eigen/MetisSupport>
#endif

using namespace std;
using namespace Eigen;

int
main(int argc, char** args)
{
	//   typedef complex<double> scalar;
	typedef double scalar;
	SparseMatrix<scalar, ColMajor> A;
	typedef SparseMatrix<scalar, ColMajor>::Index Index;
	typedef Matrix<scalar, Dynamic, Dynamic> DenseMatrix;
	typedef Matrix<scalar, Dynamic, 1> DenseRhs;
	Matrix<scalar, Dynamic, 1> b, x, tmp;
	//   SparseLU<SparseMatrix<scalar, ColMajor>, AMDOrdering<int> >   solver;
	// #ifdef EIGEN_METIS_SUPPORT
	//   SparseLU<SparseMatrix<scalar, ColMajor>, MetisOrdering<int> > solver;
	//   std::cout<< "ORDERING : METIS\n";
	// #else
	SparseLU<SparseMatrix<scalar, ColMajor>, COLAMDOrdering<int>> solver;
	std::cout << "ORDERING : COLAMD\n";
	// #endif

	ifstream matrix_file;
	string line;
	int n;
	BenchTimer timer;

	// Set parameters
	/* Fill the matrix with sparse matrix stored in Matrix-Market coordinate column-oriented format */
	if (argc < 2)
		assert(false && "please, give the matrix market file ");
	loadMarket(A, args[1]);
	cout << "End charging matrix " << endl;
	bool iscomplex = false, isvector = false;
	int sym;
	getMarketHeader(args[1], sym, iscomplex, isvector);
	//   if (iscomplex) { cout<< " Not for complex matrices \n"; return -1; }
	if (isvector) {
		cout << "The provided file is not a matrix file\n";
		return -1;
	}
	if (sym != 0) { // symmetric matrices, only the lower part is stored
		SparseMatrix<scalar, ColMajor> temp;
		temp = A;
		A = temp.selfadjointView<Lower>();
	}
	n = A.cols();
	/* Fill the right hand side */

	if (argc > 2)
		loadMarketVector(b, args[2]);
	else {
		b.resize(n);
		tmp.resize(n);
		//       tmp.setRandom();
		for (int i = 0; i < n; i++)
			tmp(i) = i;
		b = A * tmp;
	}

	/* Compute the factorization */
	//   solver.isSymmetric(true);
	timer.start();
	//   solver.compute(A);
	solver.analyzePattern(A);
	timer.stop();
	cout << "Time to analyze " << timer.value() << std::endl;
	timer.reset();
	timer.start();
	solver.factorize(A);
	timer.stop();
	cout << "Factorize Time " << timer.value() << std::endl;
	timer.reset();
	timer.start();
	x = solver.solve(b);
	timer.stop();
	cout << "solve time " << timer.value() << std::endl;
	/* Check the accuracy */
	Matrix<scalar, Dynamic, 1> tmp2 = b - A * x;
	scalar tempNorm = tmp2.norm() / b.norm();
	cout << "Relative norm of the computed solution : " << tempNorm << "\n";
	cout << "Number of nonzeros in the factor : " << solver.nnzL() + solver.nnzU() << std::endl;

	return 0;
}
